NLP QR

We are seeking a highly skilled NLP Quant Researcher to join our quantitative research and trading organization. In this role, you will design, develop, and deploy state‑of‑the‑art natural language processing models that power predictive signals, trading strategies, and real‑time decision systems across global markets. You will work in a deeply technical, research‑driven environment alongside quantitative researchers, data scientists, and engineers to transform unstructured text into high‑value alpha.

• Develop and evaluate NLP‑driven predictive signals using diverse text sources including news, filings, earnings transcripts, broker commentary, and social media.
• Build and iterate on large‑scale language models, transformer architectures, embeddings, and sequence models tailored for financial forecasting.
• Perform rigorous statistical testing, backtesting, feature engineering, and model validation to drive measurable improvements in signal quality.
• Explore new research directions in LLMs, retrieval‑augmented modeling, sentiment extraction, topic modeling, event classification, and alternative NLP paradigms.

• Architect efficient data ingestion, cleaning, enrichment, and labeling pipelines for high‑volume, low‑latency textual datasets.
• Work with engineering teams to deploy NLP models into production trading environments, ensuring they meet performance, latency, and reliability requirements.
• Scale research workflows using distributed computing, high‑performance clusters, and modern ML stacks.

• Partner with quantitative traders and portfolio managers to translate textual signals into actionable trading strategies.
• Collaborate with software engineers to integrate models into production‑grade systems.
• Contribute to the continuous improvement of the research platform, tooling, and scientific methodology.

• Advanced degree (PhD or MS) in Computer Science, Applied Mathematics, Statistics, Computational Linguistics, Machine Learning, or a related field.
• Strong background in NLP, including experience with transformers, deep learning frameworks, tokenization, embeddings, and modern foundation models.
• Proficiency in Python and ML ecosystems such as PyTorch, TensorFlow, JAX, Hugging Face, or similar.
• Solid understanding of probabilistic modeling, statistical inference, time‑series forecasting, and machine learning theory.
• Experience working with large‑scale datasets, distributed computing, and cloud or HPC environments.
• Ability to design and execute empirical research with a disciplined, scientific approach.

• Experience applying NLP or machine learning in financial markets, especially in alpha discovery or automated trading.
• Familiarity with market microstructure, order‑book dynamics, or event‑driven trading.
• Experience with reinforcement learning, sequence‑to‑sequence modeling, or multi‑modal ML (e.g., text + structured data).
• Contributions to NLP research (papers, open‑source projects, model benchmarks).

• The opportunity to work on cutting‑edge research with significant commercial impact.
• A culture that values scientific rigor, innovation, and technical excellence.
• Access to state‑of‑the‑art compute, large proprietary datasets, and a collaborative cross‑disciplinary team.
• Highly competitive compensation aligned with experience and impact.